Start with Data started out as a product data technology consultancy specialising in PIM technology platform evaluation, selection and due diligence, working with a number of clients in the retail, distribution \u0026 FMCG sectors

Start with Data

Using Data Analytics Effectively In Marketing - Ambitious PR Data analytics in marketing is essential for any modern business. If you don’t have data analysed in the right way, you can’t understand your customers, and if you can’t understand them you can’t reach them. So, if data is good, data must be better, right? Not necessarily. As puts it: “We have huge lakes of data that are filled with dead fish”. Whether you’re designing a mailing list or training a neural net, you should never start with data first. more Rishad Tobaccowala Many businesses gather as much data as they can, pile it all in a database, then try to think of ways to use it. This is as backward as buying an encyclopedia then wondering if there’s anything you can look up in it. As marketers, . This can be much harder than it sounds! our first job is to work out what questions we need to ask What can data analytics in marketing tell you? Data can only ever answer with numbers. It can tell you exactly how many people used your website in the past month and where they came from, but it can’t offer any qualitative insight. Marketers, therefore, need to break questions down to a point where they can be answered with numbers alone. For example, you might be looking to increase traffic to your website. No matter how much data we have, it can’t tell us how to do this. Our job is to design questions that, when answered, might point out how we can achieve this, such as: How many people visited our website last month? Which sites did they arrive from? What demographics do we see the most of? Which web pages are the most visited? Keep in mind that we haven’t looked at any data just yet. We starting out with a big pile of data and trying to work out what it can tell us, we’re coming up with questions first and then looking for the answers. aren’t Select the data you require Now, with our list of questions in hand, it’s time to find some answers – let’s get some data! Think about what information you need to know in order to answer the question, then how you can get it. For instance, if we want to know where our site users have arrived from, we’ll need to set up some form of a tracking system. If instead, we wanted to know about the types of products that are often bought together, we could gather the information from our sales database instead. Quality is important, and you’re often better off getting detailed, relevant information on a targeted audience than casting your net as wide as possible. As we’ve explored previously, customers are often willing to give you their data if they trust you, so you should use this to build up a nuanced picture of your audience. Consider too, what data does google analytics prohibit collecting? What data analytics KPIs do you need? Add human perspective Going back to our first example, we now know how many people use our site, which pages are most popular, where referrals come from, and so on. Now we can interpret them: Tobaccowala highlights the importance of human insight using what he calls ‘The Four P’s’: Perspective, Provocation, Point of View, and Plan of Action. It’s our job, as humans, to turn the dry data into useful perspectives. Digital marketing data analytics can tell us that our website is getting a lot of referral traffic from Facebook – our insight is that this may be an area to focus marketing spending on. This insight generates further questions – how much should we spend on Facebook advertising? Which demographics are the best ones to target? Can we produce better content for the platform? Go back to the start and repeat the same steps we’ve just been through until you have specific actions you can take to achieve your overall goal. This process is a cycle, and it’s crucial to making informed decisions about your marketing strategy. A case in point Take the example of Sinan Aral’s work with Jet, an online sales platform. His goal was to improve overall sales. Rather than dive into the data, he came up with specific questions to ask – “which products are often purchased together?”, and “which products are rarely purchased together?”. Simple questions with quantitative answers, which he could answer by turning to sales data. A simple tweak to the ‘suggested products’ algorithm improved the likelihood of displaying shoppers products they’d be interested in purchasing, ultimately improving sales. This approach is efficient, cost-effective, and it makes use of marketers’ most important asset; their minds. The cycle of data-driven research delivers consistent results, and allows marketers to demonstrate real value to clients and management: Set a high-level goal. Identify specific questions with quantitative answers – “how much”, “how many”, “how long”, etc. Acquire data that can answer these questions. What do these answers tell you? If there are further questions, begin the cycle again. In the digital age, brands must be able to use data effectively and efficiently. If you need help with data-driven insights and creative thinking, we’re always ready to help.

Using Data Analytics Effectively in Marketing

In this blog, guest writer Feifei Cai (Senior Software Engineer), Hao Zhu (Senior Software Engineer), shares the practice of using Alluxio and Spark to accelerate the auto data tagging system in WeRide, an autonomous driving technology company. The original content was published on Alluxio's Blog (Disclaimer: The author is a Founding Member @Alluxio). About WeRide is a global leading company that develops L4 autonomous driving technologies. Established in 2017, WeRide is headquartered in Guangzhou, China, and maintains R&D and operation centers in Beijing, Shanghai, Nanjing, Zhengzhou, Shenzhen, as well as San Jose in the USA. WeRide WeRide launched China’s first commercial Robotaxi service, completely open to the public in 2019. Now WeRide offers an all-rounded product mix on the Robottaxi, Mini Robotbus, and Robotvan. We provide multiple services including online ride-hailing, on-demand transport, and urban logistics. Why Alluxio There are three major benefits Alluxio provides – it’s fast, simple, and easy to use. First, Alluxio is fast as it provides distributed caching. Memory speed I/O is provided by Alluxio's caching service. In addition, the tiered storage can leverage both memory and disk, making it elastic to scale data-driven applications in a cost-effective way. Second, Alluxio provides simplified and unified data access and data management. Finally, it's very easy to deploy and use Alluxio in the existing data platform. For example, Spark can run on top of Alluxio without any code changes, just one line of configuration change. How Are We Using Alluxio at WeRide Autonomous Driving Development The diagram above shows a typical workflow of autonomous driving development in WeRide. We start with data collection, collecting sensor data from the test vehicle fleets every day. Once the data is collected, we upload it to the cloud. The data is then processed and analyzed. Afterward, we will do data labeling, HD map labeling, and development. Next, we will perform machine learning model training for AI algorithm development. Simulation and validation will also be conducted. Finally, we will deploy both on-boarding vehicle fleets and off-boarding servers. We have both hardware and software in the loop. The biggest challenge is the size of the data. We generate many terabytes of data every day and have accumulated millions of kilometers of mileage, so the entire size is in petabytes. This requires vast amounts of storage and significant computing power. Data Tagging Tags are sometimes confused with labels. In autonomous driving, tags usually mean scenarios, while labels can be obstacles or boxes. For example, the left image below shows a lane-change scenario and the right image is a right-turn scenario. We will tag a sequence of frames with lane-change or right-turn tags. The labels here are the lane, the surrounding vehicles, obstacles, and the traffic lights, which are different from tags. In a data-driven autonomous driving workflow, data tagging plays an essential role in data analytics. With the tagging results, we can do data selection to support the downstream pipelines, including model training, benchmark, and long-tail evaluation. Alluxio + Spark in Kubernetes An end-to-end tagging pipeline is to collect and upload the ROS (Robot Operating System) bag files from the test vehicles and then convert them into Parquet format. After that, we can run Spark jobs to perform data analytics algorithms. From there, we can export the tagging results to downstream pipelines. As can be seen above, the system architecture is quite typical. We build and run applications on hybrid cloud with both public cloud and on-prem datacenters with the following considerations: the on-prem datacenter is more cost-effective and can be customized, while public cloud is easy to scale. In both public cloud and on-prem data centers, we use Kubernetes to manage the computing resources of the CPU and the GPU clusters and to schedule the workflow and tasks. In the storage layer, we use MinIO to build S3 compatible object storage service. Alluxio provides a fast, simplified, unified data layer that bridges S3, MinIO and Spark, and other data analytics applications so that the data tagging workflow is accelerated with the introduction of Alluxio. Test Results We followed for the deployment. We used the helm chart to deploy a test cluster across the CPU and GPU clusters for 42 workloads. We used tiered storage - level zero is the memory with 50G, and level one is the SSD with 200G. Based on 42 worker nodes, we ended up with 2TB of memory and 8TB of SSD. Alluxio documentation For the data tagging tests, there's only one line of a configuration change, which is very easy to use. We have added Alluxio into the command-line tools, but Alluxio is enabled by default and hidden from the end-users. Overall, the test shows approximately . 7x faster in performing tagging tasks with the introduction of Alluxio Future Work Use Alluxio as a Unified Data Orchestration Layer We plan to build the unified data layer with Alluxio as the data orchestration layer. Our goal is to integrate with more applications, including Presto, HIVE, and TensorFlow, which are the services already running inside WeRide. Furthur Accelerate the Workflow with Spark + GPU + Alluxio We are integrating Spark, RAPIDS, and GPU with Alluxio to get more acceleration, not just for auto data tagging but also in many other applications. (Disclaimer: The author is a Founding Member @Alluxio)

How to Accelerate Auto Data Tagging for Autonomous Driving in Hybrid Cloud

Photo by Jens Johnsson on Unsplash So, you’d like to spice things up in your current business strategy and add an extra layer of high technology with a sophisticated AI solution. Let me just ask you one simple question first: Are you sure it is the best move? If your eyes sparkle with excitement as you think “Yes!”, you can skip this article and go find your perfect AI vendor. If, however, you have even a shade of doubt — stick around. I promise that you won’t regret it! What I have for you is answered by Miquido’s Head of Innovation that should or financial suicide for your business. Ok, no more waiting — here are the questions: 10 questions help anyone understand whether adopting AI technology is the right move Can you clearly? state your problem Can you ? solve your challenge without AI What do you understand by “ ”? doing your task right Do you accept that ? your solution will never be perfect Do to get started? you possess enough data Is your ? data relevant Will you have ? continuous access to new data Can you l ? abel your data correctly or is accuracy enough? Do you need an interpretable model Do you have to keep the project going? enough resources Sound simple enough? Well, it gets a bit more complicated. So we’ll look into each of them in more detail in just a second. Get comfy, grab something to take notes with, and go along with this article, or simply save it for later. Let’s dive in! 1. Can you state the problem clearly? A clear vision of what you want to achieve is a must when it comes to applying AI in business. You need to have a specific problem that requires high-tech solutions that can be solved with Artificial Intelligence and/or Machine Learning. Introducing AI for the sake of bragging about having it won’t fly here. To help you answer this question, try thinking about specific challenges you want to address and always have your target audience in mind. Ask yourself: Which repetitive tasks are you trying to automate? Who will benefit from this automation? Will it be worth the investment? Pro tip Its ultimate goal is to help you get inspiration and once your idea is stated, you can come back to Machine Learning. Focus on what you can have right there and right now — the cornerstone of Artificial Intelligence, data. If you’re not sure where to start — start with data mining. And then ask yourself the next question: 2. Can you solve your challenge without AI? Artificial Intelligence is great because it offers seemingly simple solutions to complex problems. The only issue is that, in fact, it’s far more demanding than it appears. So the ugly truth about the development of AI technology is that if you can get on without it — just do it. Consider these questions: Is your problem complex enough to engage machine learning? Could it be represented by a mathematical equation? Can you think of a step-by-step recipe for an output? If you answered “yes” to the last two questions — congratulations, you need artificial intelligence! don’t If, however, you couldn’t quite wrap your mind around the correct patterns, advanced machine learning algorithms might be your only chance to succeed. Pro tip It’s quite simple, really: . if you can recognise the pattern yourself, you don’t need Machine Learning to do that for you By now, you should have a pretty good understanding of whether you actually AI in your strategy or if you simply want to follow the trends. Let’s see if Artificial Intelligence actually help you out. need can 3. What does it mean to do your task right? First of all, you’ll have to define what “right” and “wrong” mean to you and your business. The world of technology is still pretty binary, and if something isn’t “true”, it false by definition. is So, before investing in any solution, . make sure you understand what it is you hope to gain from it Artificial intelligence is, first and foremost, a complicated algorithm. And in order for it to learn, you need to be able to evaluate its performance. Think along the lines of: How will you evaluate that a task is done correctly? Which mistakes will be more harmful than others? How many mistakes per 1000 results can you afford to have? That’s another big revelation about working with AI: . mistakes are unavoidable Pro tip Be careful not to aim too high with accuracy. . Placing an excessively high bar may result in you missing some profitable opportunities That brings us straight to question #4: 4. Do you accept that your solution will never be perfect? If your reply to this question is “no”, I’ve got some bad news for you: you’re not ready to work with machine learning. Sometimes more, sometimes less, but there’s no chance in the world for your solution to run error-free. Even if you have Elon Musk on your team. There will be mistakes. At this point, think about yourself, your mental health, and the daily struggle you’ll have to accept from now on in your business: Can you live with occasional mistakes in your model? What are the actual consequences of such mistakes? What does it all mean from an ethical standpoint? Once again, there are two potential outcomes here: You’ve evaluated the risks correctly, talked yourself into accepting them, and are on your merry way to hiring an AI software development company. You’ve reached the conclusion that the stakes are too high, so you can either give up on the idea of working with machine learning altogether. Pro tip To minimise the chance of costly and dangerous mistakes, simply . This is known as a “human-in-the-loop approach” and can save you some headaches. have someone who will double-check the results With that in mind, you must have a pretty good idea of whether or not the AI onion is worth peeling. Now it’s time to see if you’ll be able to actually build your model. 5. Do you have enough data? The most common and broadly discussed limitation of Artificial Intelligence is its heavy dependency on datasets. There is simply no machine learning without data. To put it bluntly: if you don’t have the data to keep your project running, your chances of launching it in the first place are slim. Some questions that might help you think are: Do potentially useful inputs even exist? Can you gain access to them (e.g. build them, buy them, etc.)?Do you have enough examples? Pro tip It usually takes at least 10 thousand samples when training the model from scratch. Yet, , and don’t we all thrive for perfection? the more examples you have the more reliable your AI model will be In data science, however, it’s both quality quantity that matter, so if you do have enough materials to proceed with, let’s check if they hold any value. and 6. Is your data relevant? As you could’ve guessed from the previous point, without having a well-thought-through plan for data collection, you’ll stumble across multiple serious issues with your AI solution pretty quickly. The best way to ensure it doesn’t happen is to double-check the actual relevance of your data. There’s no point in having 1000+ features with no practical use, they’ll only eat up your precious storage space. The important questions to keep in mind to ensure that you’ll only work with the highest quality data points are: Are your data points significant? Is all the data clean enough? Is the data you have relevant to your target audience? Is it free of bias? Pro tip . In order for everything to work in an orderly fashion, you need to ensure your sample data is well-balanced, clean and free of inconsistencies. To learn from examples, AI needs good examples to learn from And if you’ve got that covered, it’s time to think about the further evolution of your AI solution. 7. Will you be able to access new data continuously? We are almost done with data questions, I promise! But while we’re on the subject, let’s think about the scalability of your project. Having an accurate initial model might suffice for obtaining reliable predictions for a relatively short period of time. Yet, you’ll soon notice there are numerous factors that may negatively affect its performance. These threats include social events, seasonal changes, shifts in demographics, the geographical location of your users, etc. When there are factors that can influence your target audience over time, . your AI model needs to be constantly retrained Here, you will have to face new challenges and try to answer questions like: How sensitive is your dataset to changes? Do or will you have access to new data continuously in order to update it? A great example of such unexpected threats is Covid-19. It has pretty much rendered a massive amount of data obsolete, as people’s behaviour has changed drastically. Pro tip Or, in case of a disastrous setback, like a worldwide pandemic, at least can be easily updated. Make sure that your AI solution will be weather-proof. That brings us to the final question about data. 8. Can you label your data properly? We’ve covered some paramount issues like obtaining, updating, and navigating Big Data, so now it’s time to talk about data management. . Some datasets, such as image recognition systems, inherently contain labels based on the logged user actions. However, if your plan is to build a classification from the ground up, you’ll need to come up with a system for correct data labelling. In order for your AI solution to correctly understand the data, it requires proper labelling The important questions to think about here are: Do you to label your dataset? need Can you get a human to do it? How much time and money will that process require? Pro tip When working with advanced data, such as ECG signals or medical images, before your model could learn from examples. hire experts to correctly classify each case With that, we’re ready to move on to the next question! 9. Does your model have to be interpretable? is one of the primary issues with machine learning. But what does it even mean? Interpretability In the simplest terms, the higher the , the easier it makes it for a human to understand the processes behind the algorithm’s decision making. interpretability of an AI training model It is crucial for some businesses to fully understand the flow due to external policies and regulations. However, more often than not, . having a model that is accurate yet not entirely interpretable, is enough to get your AI project up and running So, the two major questions that arise here are: Are there any regulations governing your model’s interpretability? What is your trade-off between accuracy and interpretability? Pro tip Testing helps to launch many projects regardless of their complexity. If you can’t explain how something works, run as many tests as needed to prove that it does work. Looks like it’s time for the final and the most important question you should ask yourself before committing to an AI project: 10. Do you have enough resources to implement & maintain your AI solution? As you may already know, machine learning projects aren’t particularly cheap, are not that easy to implement, and require an experienced team behind the wheel. So before you jump into your next idea headfirst, try to evaluate with great care whether you have enough resources to really pull this off. Some of the questions that might help you at this stage are: How much does building and updating the database cost? Do you have access to enough processing power? Are you aware of the costs of training and retraining your model? Are the expected benefits higher than the estimated costs? Depending on the complexity of your model, all these numbers can vary drastically. Pro tip G , as these solutions generally tend to be much more cost-effective than self-hosted solutions. o with Cloud Final remarks With all that in mind, you should be perfectly ready to make a final decision regarding your AI project. If you’re still up for the challenge — that’s great news! All there’s left to do is to find the right vendor to deliver your AI solution. And if you’ve discovered it may not be the best time for one just yet — don’t worry! The world of technology is evolving rapidly, so while something might have been too complicated yesterday, it may as well become the new normal tomorrow. Just remember — as long as you can get on Machine Learning, there’s really no pressing need to spend resources on it. without About The Author This article was written by Anna Kvasnevska, and has been posted on her behalf, with her permission. Anna is a Millennial, an ex-pat, a writer, a marketer — in that particular order. On a path to understanding the world through personal & spiritual growth.

10+ Questions you Should ask Yourself Before Developing an AI Solution

An experiment in real-time data visualization About a year ago, I was right in the middle of training for my 4th triathlon and just picked up a new Garmin watch along with all the sensors I deemed necessary at the time. I started to really monitor my heart rate while I was training and would adjust my activity level to make sure I kept it in a sustainable zone. At the same time, I was working on implementing Google Analytics on a website the team at was about to launch and brainstorming some cool ways to monitor the success of the site. My goal was to create an interesting animated visual using the Google’s Real-Time Reporting API . 20nine (in beta) I realized that a heart rate could potentially be just as good of an indicator of activity level for fitness as it could be for a website launch or marketing campaign. What if I could translate the real-time website analytics into an EKG style chart? This would be a fun and interesting way to see, at a quick glance, how a campaign was doing. I started doing some research to see how I could merge the two ideas. My first stop was what an EKG looked like for a normal heartbeat. I used the diagram above as my basis for the design. To differentiate between states I started looking at different EKG charts. I learned a little bit about the various heart rhythms and what they meant. In an attempt to keep this data visualization simple, at least for the first iteration, I decided to keep it to 4 different states/rhythms: Asystole (flat) Bradycardia (Some activity, but low) Normal (Healthy activity) Tachycardia (High activity level) The Development Before diving into the code, I needed to figure out how I was going to match up website activity to the above heart rhythms. The combination of traffic, events and conversions was going to be the best gauge at the health of the campaign, but they would need to be weighted according to importance. The graph should generally work on any website so, when initially loaded, it will always need to start with data collection to get an idea of the normal activity. The algorithm will need to get all the data points , weight them accordingly and save it in an array. (traffic, events and conversions, etc..) At the next interval, the algorithm would run and check the current value against the averages of the past to determine what the rhythm is. This isn’t perfect, but fine for a proof of concept. Step one was setting up the Google Analytics Real-Time reporting API through Google’s developer console. Once the Google API was authenticated, I’m querying two reports: and . Due to query restrictions in the API documentation, we can’t get this information in one API call. I had to stagger these requests and then call my Heartbeat function. active users goal completions Check out this guide for documentation on getting started with the Reporting API: https://developers.google.com/analytics/devguides/reporting/core/v4/quickstart/web-js The D3.js line graph ended up being the best bet to create the look of the heart rhythms. There was a lot of trial and error to get the speed and line graph to resemble an EKG, but what worked best was separating the states into functions with an array of hard-coded values. Here’s an example of what the normal rhythm function looks like: The Demo Here’s a video of the visualization in action. Thanks For Reading! You can find the full code here to adjust and try on your own analytics setup: https://github.com/jzeoli/heartbeat

Google Analytics Heartbeat Data Visualization

An illustrated guide to becoming a Web Developer with links to relevant courses Hello Guys, I hope you are doing great in 2019. I am sure you all have already made your goals by now and thinking about how to achieve those goals. If one of your goals is to learn to code or to become a in 2019 then I have something great to share with you today, the . Web Developer 2019 Web Developer RoadMap This is an excellent illustration on how to become a web developer in 2019 and guide you on how you can learn and master Web Development in 2019. It’s a collection of different paths to learning front-end, back-end, and all other things a Web developer should know. You will not just know about web development but about which is important for all kind of programmers. essential programming stuff The RoadMap is also divided into 3 sections, the first section is about which every web developer should know, while the second and third section is about and development, two main areas of web development. common skills front-end back-end You can either learn Front-end or Back-end or both, depending upon your choice. If you want to become a then you have to learn both front-end and back-end, there is no escaping there. FullStack Web developer In Frontend also you can choose to learn the framework you want like , or , you don’t need to learn them all but learn the one you like. If you ask for my recommendation, Vue.js is a better choice in 2019 than the other two but again those are more used than Vue.js yet. React Angular Vue.js Btw, you might have seen this RoadMap before. This awesome visual guide to becoming a web developer was created by and posted on his GitHub repository . It’s one of my favorite places and I have bookmarked it for a repeated visit. Kamranahmedse here Last year, while doing some internet surfing I stumble upon this excellent page. I first found the there and become an instant fan of . In fact, I have these RoadMap printed and stick to my Desk for regular reference. DevOps RoadMap Kamran Ahmed Btw, the Roadmap tells you what to learn but doesn’t tell you how to learn and where you can learn those skills. I have tried to supplement that part by providing links of some useful and you can take to learn the , , , and required to become a web developer and mentioned in this RoadMap. courses books technologies programming languages frameworks libraries How to become a Web Developer in 2019 Anyway, Here’s where you’ll start. As I have said before, You can choose either the Front-end or Back-end path below, depending upon your interest. Regardless, there are eight recommendations in yellow that you must learn for either path, these are the common things every programmer should know. https://github.com/kamranahmedse/developer-roadmap Recommended learning for either path One of the most popular version control system. It’s just not possible to live without Git anymore. You can check out on Udemy to start with. 1. Git The Git Complete Guide It allows you to remove login to another host, another popular networking concepts which every web developer should know. 2. SSH The HTTP protocol is the backbone of web and a good knowledge of both HTTP and HTTPS is mandatory for a web developer 3. HTTP / HTTPS Not just a web developer but for any programmer, the Linux command line is very very important and I strongly recommend you to spend some time to learn them. The course on Udemy is a good place to start with and if you like free courses, you can use these f to start learning Linux. 4. Basic Terminal Usage Linux Command line Basics Linux command line basics ree Linux courses These are the building blocks of any program and better knowledge of Algorithms and Data Structure is key for your next job or doing well on your current job. 5. Data Structures and Algorithms If you are interested in learning algorithms and data structure, I suggest you choose the course which is in the programming language you understand. For example for Java developers is a good one to start with. Data Structures and Algorithms: Deep Dive Using Java Similarly for JavaScript Developers Colt Steele’s is a good one. JavaScript Algorithms and Data Structures Masterclass And, if you use Python then is a nice one. Algorithms and Data Structures in Python And, if you don’t mind learning from resources then you can also use this list of to start with. free algorithms courses If you are creating global applications which shows information in many different languages across the world then you should have a good knowledge of character encodings. It basically tells your browser how to show your data. 7. Character Encodings There is no doubt that every programmer should know Git and Github as they are the standard in terms of version control and code repository. If you want to learn and master and , you can check these . 8. GitHub Git Github free Git course The 2019 Frontend Developer RoadMap If you want to become a front-end developer in 2019 then you should follow the below Front-end Developer RoadMap. You should focus on yellow boxes which highlight important things to learn and follow from there. Below the RoadMap, you will find additional resources like books and courses to aid your learning. https://github.com/kamranahmedse/developer-roadmap/blob/master/images/frontend.png 1. Web Development Basics It’s pretty obvious that if you want to become a Web Developer then you should know the basics of internet, web applications, protocols like HTTP and web development in general. Thankfully there are a lot of courses where you can not only learn these but much more than that. I recommend the following resources for all programmers who want to learn Web Development: The Web Developer BootCamp by Colt Steel The 2019 Complete Web Developer Bootcamp by Angela Yu The Advanced Web Developer Bootcamp 2. HTML and CSS HTML and CSS are the backbones of any website. HTML provides the structure while CSS provides the style and help them to look better and more visually appealing. If you want to become a serious frontend developer then you must master these two and to start with, you can check these to learn online. free HTML and CSS courses 3. JavaScript Just like four pillars of object-oriented programming, I mean , , , and , there are three main pillars of web development, namely — HTML, CSS, and JavaScript. Abstraction Encapsulation Polymorphism Inheritance The first two provides structure and style but it’s the JavaScript which makes them alive by adding interactiveness. It’s extremely important for a modern Java developer to learn and thankfully there are many courses which you can look to learn both beginner and advanced JavaScript, here are some recommendations: JavaScript The Complete JavaScript Course 2019 A Beginner’s Guide to Advanced JavaScript and ES6 4. TypeScript Just like we have C and , TypeScript can be considered as JavaScript++ though it’s not as popular as C++. C++ The good part of is that it adds type-safety in JavaScript code which means you can catch nasty JavaScript type related errors in the development phase. TypeScript It also makes developing object-oriented code easily for JavaScript. If you want to learn TypeScript, is a great course to start with and if you need more choices or free alternatives, here is another list of some to start with. Understanding TypeScript free TypeScript courses 5. Angular Gone are the days when people build websites using plain HTML, CSS, and JavaScript. Nowadays most of the work is done by a framework like , or Vue.js. Angular React JS They not only provide a structure for storing your code but also makes it easy to develop a web application in quick time. Angular is backed by Google, hence learning Angular is a good choice for any modern Web Developer. If you want to learn Angular there is no better course than Max’s on Udemy. Angular 7 (formerly Angular 2) — The Complete Guide 7. React JS Similar to , is also a popular library for developing web applications. It allows you to write reusable components which you can then use to create modern looking interactive web pages. Angular React Like Angular is backed by Google, React is backed by Facebook and hence quite popular. If you want to learn React then you either join Stephen Grider’s or Max’s on Udemy. Both are excellent courses to learn to React online. Modern React with Redux React — The Complete Guide Here are the links: Modern React with Redux React — The Complete Guide 8. Vue.js The Vue or Vue JS has really picked up speed last year and more and more people are recommending and using Vue.js. I am still a beginner on Vue and probably take some time to learn and migrate into Vue based application. But, if you have already made a decision you can start with course on Udemy. Vue.js Essentials The 2019 Backend Developer RoadMap If you want to become a back-end developer in 2019 then you can follow this Back-end Developer RoadMap. Again, you can start with yellow boxes which highlight key you need to learn and then grow from there. Below the RoadMap, you will find some supplementary material and links to courses and books to aid your learning. I will also earn some commission if you end up buying any non-free courses. technologies https://github.com/kamranahmedse/developer-roadmap/blob/master/images/backend.png 1. Node.js There are many choices to develop your web application’s backend e.g. you can use Java and Spring or maybe + but in recent year Node.js has come up as the preferred option. Python Django The best thing about is that it allows you to develop a complete web application using just one programming language — JavaScript. NodeJS If you want to go Node route, then by and is best to start with. The Complete Node.js Developer course Rob Percival Andrew Mead 2. Java I am primarily a Java developer who have learned Web Development and other programming languages, hence when it comes to developing backend my preferred choice is Java. You can either use or Boot to write a cutting edge, high-performance backend. If you want to go Java and Spring route, here are a couple of courses you can join to get up-to-speed. Core Java Java + Spring The Complete Java Masterclass Spring Framework 5: Beginner to Guru 3. Python is another awesome language, full of powerful framework and libraries. If you want to develop backend using then you can use Django, a full-stack web development framework for Python programmers. Python Python To start with here are a couple of courses to learn Python and Django: The Complete Python Bootcamp Python and Django Full Stack Web Developer Bootcamp 4. Ruby Ruby is another powerful language perfect for web development. It has got a great framework called Rails which makes creating Web applications really easy and fast. If you want to start with Ruby you can check out on Udemy. The Complete Ruby on Rails Developer Course It’s one of the best resources and if you need more choices and don’t mind learning from free resources, you can also take a look at these . free Ruby and Rails courses 5. PHP and MySQL Forget about whatever negative things people say about PHP. It is still the easiest way to write the server side code and many, almost 50% of the internet runs on PHP and MySQL stack. If you are interested in learning PHP, you can check out course on Udemy. If you need more choices and free resources, you can also check my list of to start with. PHP for Beginners free PHP and MySQL courses That’s all about . This is an excellent resource to start with as it highlights several paths to become the Web developer you always wanted to be. how to become a Web Developer in 2019 Though, if you stuck somewhere and if there is any confusion, you can always ask here and we’ll try to answer your queries as best as possible. If you are new into the world, don’t let this illustration and a lot of technologies scare you. It’s just a guideline and you can take a different path to learn Web Development and become a Web Developer, you can choose the path you like and stick to that. programming Other you may like Programming Articles 10 Things Java Programmer Should Learn in 2019 10 Programming languages You can Learn in 2019 10 Tools Every Java Developer Should Know 10 Reasons to Learn Java Programming languages 10 Frameworks Java and Web Developer should learn in 2019 10 Tips to become a better Java Developer in 2019 Top 5 Java Frameworks to Learn in 2019 10 Reasons to Learn Python in 2019 10 Testing Libraries Every Java Developer Should Know Closing Notes Thanks for reading this article so far. You might be thinking that there is so many stuff to learn, so many courses to join, but you don’t need to worry. There is a good chance that you may already know most of the stuff, and there are also a lot of useful which you can use, I have also linked them here and there along with best resources, which are certainly not free, but worth of money. free resources I am a particular fan of courses, not because I will get paid if you end up buying any course but because they are very affordable and provides a lot of values in a very small amount, but you are free to choose the course you want. Udemy At the end of the day, you should have enough knowledge and experience about the things mentioned here. Good luck with your Web Development journey! It’s certainly , but by following this roadmap and guide, you are one step closer to becoming the Web Developer, you always wanted to be not going to be easy If you like this article then please consider following me on medium ( ). if you’d like to be notified for every new post and don’t forget to follow on Twitter! javinpaul javarevisited All the best for your Web Development Journey and a Big thanks to for producing such awesome content. Kamran Ahmed

The 2019 Web Developer RoadMap

Hoja de ruta para desarrolladores web de 2019

UI updating, in its essential, is data change. React offers a straightforward and intuitive way to program front-end Apps as all the moving parts are converged in the form of states. Code review of Apps made with React is a bit more easier to me as I like to start with data structures for a rough expectation of the functionalities and processing logic. From time to time, I was curious about how React works internally, hence this article. Moreover, I think it never hurts to have a deeper understanding down the stack, as it gives me more freedom when I need a new feature, more confidence when I want to contribute and more comfort when I upgrade. This article will start walking through one of the critical paths of React by rendering a simple component, i.e., a <h1>. Other topics (e.g., Composite components rendering, state driven UI updating and components life cycle) will be discussed in a similar actionable manner in the following articles. : entry point of ReactElement.createElement() : workhorse of ReactElement.createElement() : entry point of ReactDOM.render() : workhorse of ReactDom.render() : create different types of ReactComponents based on element type : ReactComponents wrapper of root element Files used in this article: isomorphic/React.js isomorphic/classic/element/ReactElement.js renderers/dom/ReactDOM.js renderers/dom/client/ReactMount.js renderers/shared/stack/reconciler/instantiateReactComponent.js renderers/shared/stack/reconciler/ReactCompositeComponent.js - function call= alias~ indirect function call Tags used in the call stack: As the locations of source code files can not be obviously derived from import statement in the , I will use @ to help locating the code snippet in demonstration. flat module tree Last words, this series is based on React 15.6.2. From JSX to `React.createElement()` After using the function intensively in a React project for a couple of months, I did not even know the existence of this React.createElement() because it is masked by JSX from a developer’s point of view. In compiling time, components defined in JSX is translated by Babel to React.createElement() called with appropriate parameters. For instance, the default App.js shipped with : create-react-app import React, { Component } from ‘react’;
import logo from ‘./logo.svg’;
import ‘./App.css’;

class App extends Component {
  render() {
    return (
      <div className=”App”>
        <header className=”App-header”>
          <img src={logo} className=”App-logo” alt=”logo” />
          <h1 className=”App-title”>Welcome to React</h1>
        </header>
        <p className=”App-intro”>
          To get started, edit <code>src/App.js</code> and save to reload.
        </p>
      </div>
    );
  }
}

export default App; is compiled to: import React, { Component } from ‘react’;
import logo from ‘./logo.svg’;
import ‘./App.css’;

class App extends Component {
  render() {
    return React.createElement(
      ‘div’,
      { className: ‘App’ },
      React.createElement(
        ‘header’,
        { className: ‘App-header’ },
        React.createElement(‘img’, { src: logo, className: ‘App-logo’, alt: ‘logo’ }),
        React.createElement(
          ‘h1’,
          { className: ‘App-title’ },
          ‘Welcome to React’
        )
      ),
      React.createElement(
        ‘p’,
        { className: ‘App-intro’ },
        ‘To get started, edit ‘,
        React.createElement(
          ‘code’,
          null,
          ‘src/App.js’
        ),
        ‘ and save to reload.’
      )
    );
  }
}

export default App; which is the real code executed by a browser. The code above shows a definition of a composite component App, in which JSX, a syntax of interweaving HTML tag in JavaScript code (e.g., <div className=”App”></div>), is translated to React.createElement() calls. This component will be rendered like this: ReactDOM.render(
  <App />,
  document.getElementById(‘root’)
); normally by a file named as “index.js”. This nested components tree is a bit too complicated to be an ideal start point, so let’s forget about the code above for now look at something simpler - that renders a simple HTML element. …
ReactDOM.render(
  <h1 style={{“color”:”blue”}}>hello world</h1>,
  document.getElementById(‘root’)
);
… babeled version …
ReactDOM.render(React.createElement(
  ‘h1’,
  { style: { “color”: “blue” } },
  ‘hello world’
), document.getElementById(‘root’));
… `React.createElement()` — creating a `ReactElement` The first step does not do much really. It simply constructs an ReactElement instance populated with whatever passed down to the call stack. The data structure is as following: The call stack to achieve the purpose of this step is as following: React.createElement
|=ReactElement.createElement(type, config, children)
   |-ReactElement(type,…, props) 1. React.createElement(type, config, children) is merely an alias of ReactElement.createElement(); …
var createElement = ReactElement.createElement;
…
var React = {
…
  createElement: createElement,
…
};

module.exports = React;

React@isomorphic/React.js 2. ReactElement.createElement(type, config, children) 1) copies the elements in config to props, 2) copies the children to props.children and 3) copies the type.defaultProps to props; …
  // 1)
  if (config != null) {
    …extracting not interesting properties from config…
    // Remaining properties are added to a new props object
    for (propName in config) {
      if (
        hasOwnProperty.call(config, propName) &&
        !RESERVED_PROPS.hasOwnProperty(propName)
      ) {
        props[propName] = config[propName];
      }
    }
  }

  // 2)
  // Children can be more than one argument, and those are transferred onto
  // the newly allocated props object.
  var childrenLength = arguments.length — 2;
  if (childrenLength === 1) {
    props.children = children; // scr: one child is stored as object
  } else if (childrenLength > 1) {
    var childArray = Array(childrenLength);
    for (var i = 0; i < childrenLength; i++) {
      childArray[i] = arguments[i + 2]; // scr: multiple children are stored as array
    }

    props.children = childArray;
  }

  // 3)
  // Resolve default props
  if (type && type.defaultProps) {
    var defaultProps = type.defaultProps;
    for (propName in defaultProps) {
      if (props[propName] === undefined) {
        props[propName] = defaultProps[propName];
      }
    }
  }

  return ReactElement(
    type,
    key,
    ref,
    self,
    source,
    ReactCurrentOwner.current,
    props,
  );
…

ReactElement.createElement@isomorphic/classic/element/ReactElement.js 3. Then ReactElement(type,…, props) copies the type and props as they are to ReactElement and returns the instance. …
var ReactElement = function(type, key, ref, self, source, owner, props) {
  // This tag allow us to uniquely identify this as a React Element
  $$typeof: REACT_ELEMENT_TYPE,

// Built-in properties that belong on the element
  type: // scr: --------------> ‘h1’
  key:  // scr: --------------> not of interest for now
  ref:  // scr: --------------> not of interest for now
  props: {
    children:     // scr: --------------> ‘hello world’
    …other props: // scr: --------------> style: { “color”: “blue” }
  },

// Record the component responsible for creating this element.
  _owner:  // scr: --------------> null
};
…

ReactElement@isomorphic/classic/element/ReactElement.js The fields populated in the newly constructed ReactElement will be used directly by ReactMount.instantiateReactComponent(), which will be explained in detail very soon. Note that the next step will also create a ReactElement object with ReactElement.createElement(), so I will call the ReactElement object of this phase ReactElement[1]. `ReactDom.render()` — and render it `_renderSubtreeIntoContainer()` — attach `TopLevelWrapper` to the `ReactElement[1]` The purpose of the next step is to wrap the ReactElement[1] with another ReactElement (we call the instance a [2]) and set the ReactElement.type with TopLevelWrapper. The name TopLevelWrapper explains what it does — wrap the top level element (of the DOM hierarchy passed through render()): An important definition here is that of TopLevelWrapper: …
var TopLevelWrapper = function() {
  this.rootID = topLevelRootCounter++;
};
TopLevelWrapper.prototype.isReactComponent = {};
TopLevelWrapper.prototype.render = function() { 
// scr: this function will be used to strip the wrapper later in the // rendering process

  return this.props.child;
};
TopLevelWrapper.isReactTopLevelWrapper = true;
…

TopLevelWrapper@renderers/dom/client/ReactMount.js Please note that the entity assigned to ReactElement.type is a type (TopLevelWrapper) which will be instantiated in the following rendering steps. Then ReactElement[1] will be extracted from this.props.child. The call stack to construct the designated object is as following: ReactDOM.render
|=ReactMount.render(nextElement, container, callback)
|=ReactMount._renderSubtreeIntoContainer(
   parentComponent, // scr: --------------> null
   nextElement,     // scr: -------------->  ReactElement[1]
   container,// scr: --------------> document.getElementById(‘root’)
   callback’ // scr: --------------> undefined
) For initial rendering, ReactMount._renderSubtreeIntoContainer() is simpler than it seems to be, in fact, most branches (for UI updating) in this function are skipped. The only line that is effective before the logic processes to next step is …
  var nextWrappedElement = React.createElement(TopLevelWrapper, {
    child: nextElement,
  });
…

_renderSubtreeIntoContainer@renderers/dom/client/ReactMount.js Now that it should be easy to see how the target object of this step is constructed I will not repeat React.createElement. `instantiateReactComponent()` — create a `ReactCompositeComponent` using `ReactElement[2]` This is step is to create an primitive ReactCompositeComponent for the top level component: The call stack of this step is: ReactDOM.render
|=ReactMount.render(nextElement, container, callback)
|=ReactMount._renderSubtreeIntoContainer()
  |-ReactMount._renderNewRootComponent(
      nextWrappedElement, // scr: ------> ReactElement[2]
      container, // scr: ------> document.getElementById(‘root’)
      shouldReuseMarkup, // scr: null from ReactDom.render()
      nextContext, // scr: emptyObject from ReactDom.render()
    )
    |-instantiateReactComponent(
        node, // scr: ------> ReactElement[2]
        shouldHaveDebugID /* false */
      )
      |-ReactCompositeComponentWrapper(
          element // scr: ------> ReactElement[2]
      );
      |=ReactCompositeComponent.construct(element) instantiateReactComponent is the only function that is long enough to discuss here. In our context, it check the ReactElement[2]’s type (i.e., TopLevelWrapper) and create a ReactCompositeComponent accordingly. function instantiateReactComponent(node, shouldHaveDebugID) {
  var instance;
…
  } else if (typeof node === ‘object’) {
    var element = node;
    var type = element.type;
…

    // Special case string values
    if (typeof element.type === ‘string’) {
…
    } else if (isInternalComponentType(element.type)) {
…
    } else {
      instance = new ReactCompositeComponentWrapper(element);
    }
  } else if (typeof node === ‘string’ || typeof node === ‘number’) {
…
  } else {
…
  }

…
  return instance;
}

instantiateReactComponent@renderers/shared/stack/reconciler/instantiateReactComponent.js One thing worth noting here is that new ReactCompositeComponentWrapper() is a direct call of ReactCompositeComponent constructor: …
// To avoid a cyclic dependency, we create the final class in this module
var ReactCompositeComponentWrapper = function(element) {
  this.construct(element);
};
…

…
Object.assign(
  ReactCompositeComponentWrapper.prototype,
  ReactCompositeComponent,
  {
    _instantiateReactComponent: instantiateReactComponent,
  },
);
…

ReactCompositeComponentWrapper@renderers/shared/stack/reconciler/instantiateReactComponent.js Then the real constructor get called: construct: function(element /* scr: ------> ReactElement[2] */) {
  this._currentElement = element;
  this._rootNodeID = 0;
  this._compositeType = null;
  this._instance = null;
  this._hostParent = null;
  this._hostContainerInfo = null;

// See ReactUpdateQueue
  this._updateBatchNumber = null;
  this._pendingElement = null;
  this._pendingStateQueue = null;
  this._pendingReplaceState = false;
  this._pendingForceUpdate = false;

this._renderedNodeType = null;
  this._renderedComponent = null;
  this._context = null;
  this._mountOrder = 0;
  this._topLevelWrapper = null;

// See ReactUpdates and ReactUpdateQueue.
  this._pendingCallbacks = null;

// ComponentWillUnmount shall only be called once
  this._calledComponentWillUnmount = false;
},

ReactCompositeComponent@renderers/shared/stack/reconciler/ReactCompositeComponent.js The following steps will also create ReactCompositeComponent objects with instantiateReactComponent(), so I will call the object of this phase ReactCompositeComponent[T] (T for top). After the ReactCompositeComponent object is constructed, the next step is to call batchedMountComponentIntoNode and initialize the component instance and mount it, which will be discussed in detail in the next article. If you like this read please clap for it or follow me on Medium. Thanks, and hope to see you the next time. Originally published at holmeshe.me .

Understanding the React Source Code — Initial Rendering (Simple Component) I

I moved to Sweden 6 months ago with my girlfriend. It is a great country for expats like us, because almost everyone speaks really good English here. Even so, we would like to learn some Swedish, just to understand a bit more in daily conversation and about Swedish culture. For me this is gonna be a hobby language, so I don’t want to put too much effort into it. Let’s say, I’d like to spend max 10–20 hours and my target is to understand 80% of daily chit-chat. Is it doable? We will see! Here’s my approach — backed with some beginner-level data science. The concept What I’ll present here is a pretty unorthodox method, I guess. But sometimes you need to be creative if you want things to be easier, right? What I did: I took two popular TV shows, Friends and How I Met Your Mother. I got the Swedish subtitles of these TV shows — for all seasons, all episodes. I analyzed the most common words and sorted the words by number of occurrences. I made a cut at the top 1000 words. The question is, how far can I go, if I learn only the top 1000 words of a language. Or being more pragmatic: What percentage of Swedish conversations are covered by the top 1000 words of Swedish language? I set up a short data script to find out — and by the end of this article I’ll share it with you as well! But first, I’ll show you how I got my answer to this question. (Note: my method works with every European language.) (UPDATE: since I published this article, I got some nice tweets on Twitter from folks who had used this method before me! So it’s not so unorthodox after all! :-)) The code The first step is to get the subtitles. It can be any and as many movies as you want. I picked Friends and HIMYM, because I have already watched both series in English before and I liked the vocabulary. But if you want to speak more like Rocky, get the subtitles of the Rocky movies. (An important note about subtitles! As far as I know, using subtitles for non-profit research projects like this one is not illegal. But to stay on the safe side, I invite you to do the same that I did: ask a native speaker friend to listen and type in the whole script for you instead of downloading. ;-)) Either way you go — once you have the subtitles, you will need a tool to do the text analysis part. I’ve described how to get, install and start with data coding tools like . You could use any of those for this project. in this article Python, R, SQL and bash I chose the simplest one: . It’s so simple that the whole analysis actually needs only one line of code. On the screenshot below I broke it down to 14 lines just to make it more readable. bash So here’s my exact bash code: Github link for copy-pasting: . here Line by line What does the code do? In general, the first 9 lines will be about data cleaning and the rest 5 will do the actual analysis. Here it is line by line: Line 1: Read-in all the subtitle files that I put previously in my “friends” and “himym” folders on From now on you can imagine all the subtitles as one big temporary text file. Every further modification will be performed on this “one big temporary text file”. Here’s a small sample of that: my data server. Line 2: Remove all the lines that start with a number. (e.g. the timestamps) Line 3: Replace all the question marks, exclamation marks, dots and pipes with spaces. Line 4: Replace all the multiple spaces with single spaces. Line 5: Remove everything but alphabetical characters, apostrophes and spaces. Line 6: Remove all the unnecessary spaces from the beginning of the lines. Line 7: Turn every space into a line-break (means we will have one word per line in our file). Line 8: Remove all the empty lines. Line 9: Turn every uppercase character into lowercase characters. Now we have a clean list with all the words that were used in the TV shows. Every word appears as many times as it was used. Here’s a sample: Line 10: Sort the words into alphabetical order. Line 11: Make a list with every unique word and print the number of occurrences next to them. Line 12: Order this word-list by the number of occurrences. (The most occurrences at the top of the list.) Line 13: Remove spaces from the beginning of the lines. Line 14: Print the top 1000 lines (top 1000 used words) into a file called 1000.csv. Done! Don’t worry if you don’t get the whole script immediately. Even if it’s a pretty beginner-level data script, you still need some initial knowledge about bash to fully understand it. I’ve published 4 articles about the very basics of data coding in bash on my blog. You can read them here if you want to learn more: Learn Data Analytics in Bash — from scratch (and here is: ep#1 ) The results We already have the that we were looking for. Nice! The only question left is: how much will I understand if I learn only these 1000 words? To answer it, I need to have two numbers: top 1000 unique words I need to count the total number of words (multiple used words counted multiple times). I need to count how many times the top 1000 unique words occur in the subtitles. Comparing these two numbers will give back the usage ratio of the TOP 1000 words. Back to my code! To answer 1) I have to change Line 14 to this code: awk ‘{sum += $1} END {print sum}’ It will give you the total word count: 1 131 360 Then I’ll do the same with my freshly created 1000.csv file… awk ‘{sum += $1} END {print sum}’ 1000.csv I’ll get the total occurrences of the top 1000 words: 964 682 This is just amazing! 964682/1131360= 85.26% Yes, it means if I learn the top 1000 words from Friends and How I Met Your Mother, I will understand 85% of the words in the script! Reality check! I shot this picture at the IKEA on the weekend. All the words of the headline are understandable after learning only the top 200 words. Another interesting stat! This chart below shows how many percent (Y) you cover by learning the TOP (X) words. The result: a beautiful logarithmic function on a line chart. (This is not a trend or a smoothed line, this is the actual result!) After all this means that even if you invest the same amount of time to learn the first 1000 words as the second 1000 words, the first 1000 words will help you to understand 85%, the second 1000 only 5%. And so on. The next steps Okay, I have to admit, vocabulary is not everything. So my next steps will be: Learn the top 1000 Swedish words. At the same time do the Swedish course to get some grammar knowledge. Duolingo (Note that Swedish looks pretty similar to German and English, so I guess I won’t have serious trouble learning the grammar part.) Watch some movies with Swedish dub+subtitles, that I’ve already watched in English or Hungarian (which is my native language). This is important to match the pronunciation with the written format. And of course to learn some special expressions. And then… we will see! Don’t forget: my goal was to understand, not to fluently speak the language. Disclaimers To be frank I would not even call this project a “data science” project — as there is nothing very scientific in it. It’s just common sense and some text mining with basic data coding. And before NLP (Natural Language Processing) professionals start stoning me — I have to mention two known issues: This whole little language learning experiment of mine is based on a fair, but unproven assumption. I presumed that the vocabulary of these TV shows is pretty similar to the vocabulary of the daily discussions. Maybe this is just not true. Maybe. But it looks so logical that I’ve just decided to take this minimal risk anyway. The bash script I presented above is not 100% perfect. E.g. I didn’t implement any stemming algorithm to reduce words like “cooking,” “cooked” or “cooks” to the root format: “cook.” My estimation is that these kind of issues cause a top 5% inaccuracy in my results. And then I just asked myself: What’s the ROI (return on investment) here? Is that 5% accuracy really worth the extra effort? The answer was: definitely not.I was looking for a quick and dirty method to learn a language, so it’s kinda self-explanatory that the script I wrote for that should be quick and dirty as well. If I had spent days (and not minutes) optimizing my data script to 99.9% (and not to 95%), the whole project would have lost sight of the main objective: be time-efficient. And one more note. I haven’t tested this method yet and until I do, it will remain a hypothesis. But the learning-experiment starts now, and I promise that I’ll let you know how it worked! Also, I invite you to be a part of this and share your experience whether this method worked for you or not. (UPDATE: I’ve learnt the top 200 words — and I already feel much more confident with Swedish! :-)) Here are the top 1000 words in Swedish and in English . If you want to do the same for other languages, feel free to use my code . Free video course Do you want to learn Data Science and run similar projects? Then check out my new (free) online video course. Start here: How to Become a Data Scientist . REGISTER HERE (FOR FREE): https://data36.com/how-to-become-a-data-scientist/ Conclusion You can apply data in almost every segment of your life to do things smarter. Be creative and take advantage of the fact that there is so much available data around us — and it is so easy to analyze it and turn it into meaningful findings! And always stay critical, think about the return on investment and don’t be afraid to do quick and dirty things! If you want to learn more about data science, check my blog ( ) and/or subscribe to my ! And also check my new data coding tutorial series: . data36.com Newsletter SQL for Data Analysis Thanks for reading! Enjoyed the article? Please just let me know by clicking the 💚 below. It also helps other people see the story! _Tomi Mester_ author of data36.com Twitter: @data36_com

Learning languages very quickly — with the help of some very basic Data Science

Debugging is a skill you must employ when writing software and yet requires a uniquely different type of mindset. Software engineering (for most code) is largely process oriented, debugging is often non-linear and looks like it’s intuition oriented. But there process. is My #1 Rule in debugging is: Believe No One. It’s so easy to follow a wrong assumption when you are listening to another developer who has been debugging something to no avail who is sure the “error is in how we do foobar so we just need to change it to encode correctly” or “it’s a race condition in these two things” or “this is the code causing the problem, I just haven’t pinpointed it yet”. How many times have I been bitten spending time down the wrong path because I believed the other person and adopted their assumptions about how code worked or the root cause of the bug? Now, the only question I ask is: “How does this bug present itself?” Give me the root stack trace, the steps to reproduce it, the screenshots of the bug, the logs — however it presents itself. That’s all I want. Then I start with a clean slate, no assumptions. . A stack trace is one of the best things you can have when debugging. It literally tells you which places in the code the error is presenting itself. Yet, it’s surprising to me how many developers ignore the details of the stack trace. Don’t ignore stack traces Start first from the most recent frame and dissect that line of code carefully. Many lines of code are doing more than 1 thing and dealing with more than 1 piece of data. Confirm your assumptions about the code and the data state at that point with a debugger (or printf statements ;)), and compare that against how you believe it work. should Eliminate the possibility the error is in the logic or data in that stack frame (or confirm it), then move up a frame and repeat the process until you are at the root cause of the data or logic causing error. This is where things often go awry with novice debuggers. If you don’t have a clean crash or a stack trace, it’s very tempting to start formulating big theories as to why things are going wrong. This can happen when you are debugging things like performance issues, race conditions, etc. Start with data, not big theories. Examples: “The code is a mess in the CardProcessor module, and because I know it’s used at some point during when I click the button it must be there.” “The reason it’s slow is because writing to disk is slow so it must be where we write the files in the FileWriter package”. The danger here is wasting time going down an incorrect path. How do you know what theories are right and what is wrong? For problems like this, often people ignore valuable tools. You need the tools to give you the data to form good theories. If you are debugging C/C++, you must know how to attach gdb to a core file. If you are debugging a query performance problem, ask someone how the database query analyzer works. If the browser rendering is slow or you think you are leaking memory, use the chrome dev tools to profile. Know your tools. If it’s obvious you don’t have a lot of good data about the cause of the problem, you should be first thinking about how to get that data you formulate theories about root cause. Sometimes instrumentation tools can help, sometimes you must add logging. Again, the first place to start adding logging and instrumentation is where the error is presenting itself, (e.g. if the slowness is when you browse to the /foo page, profile that first). before Often times developers get so hung up on what they think is a problem — they’ve invested hours and hours in going down a path for debugging, . To avoid this, keep your theories as narrow and testable as possible. Start with the most likely first, and test the theory methodically, and eliminate along the way. After spending more than 30 minutes on a problem, asking yourself: am I sure this is really where the bug is? Be prepared to throw away a theory. they must be getting close It can help to bring fresh eyes to a problem — having a well documented bug (and again, your documentation should be focused on and how to reproduce it_,_ not your theories on its root cause) makes it easy to socialize it with other developers. how the problem presents itself is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising & sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

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